The Tanzanian tourism sector plays a significant role in the Tanzanian economy, contributing about 17% to the country’s GDP and 25% of all foreign exchange revenues. The sector, which provides direct employment for more than 600,000 people and up to 2 million people indirectly, generated approximately $2.4 billion in 2018 according to government statistics. Tanzania received a record 1.1 million international visitor arrivals in 2014, mostly from Europe, the US and Africa. Tanzania is the only country in the world which has allocated more than 25% of its total area for wildlife, national parks, and protected areas.There are 16 national parks in Tanzania, 28 game reserves, 44 game-controlled areas, two marine parks and one conservation area.
The objective of this competition is to explore and build a linear regression model that will predict the spending behaivior of tourists visiting Tanzania.The model can be used by different tour operators and the Tanzania Tourism Board to automatically help tourists across the world estimate their expenditure before visiting Tanzania.
The dataset describes 6476 rows of up-to-date information on tourist expenditure collected by the National Bureau of Statistics (NBS) in Tanzania.The dataset was collected to gain a better understanding of the status of the tourism sector and provide an instrument that will enable sector growth. The survey covers seven departure points, namely: Julius Nyerere International Airport, Kilimanjaro International Airport, Abeid Amani Karume International Airport, and the Namanga, Tunduma, Mtukula and Manyovu border points.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
tz = pd.read_csv('Train .csv')
tz.head(10)
| ID | country | age_group | travel_with | total_female | total_male | purpose | main_activity | info_source | tour_arrangement | ... | package_transport_tz | package_sightseeing | package_guided_tour | package_insurance | night_mainland | night_zanzibar | payment_mode | first_trip_tz | most_impressing | total_cost | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | tour_0 | SWIZERLAND | 45-64 | Friends/Relatives | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Friends, relatives | Independent | ... | No | No | No | No | 13.0 | 0.0 | Cash | No | Friendly People | 674602.5 |
| 1 | tour_10 | UNITED KINGDOM | 25-44 | NaN | 1.0 | 0.0 | Leisure and Holidays | Cultural tourism | others | Independent | ... | No | No | No | No | 14.0 | 7.0 | Cash | Yes | Wonderful Country, Landscape, Nature | 3214906.5 |
| 2 | tour_1000 | UNITED KINGDOM | 25-44 | Alone | 0.0 | 1.0 | Visiting Friends and Relatives | Cultural tourism | Friends, relatives | Independent | ... | No | No | No | No | 1.0 | 31.0 | Cash | No | Excellent Experience | 3315000.0 |
| 3 | tour_1002 | UNITED KINGDOM | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | No | 11.0 | 0.0 | Cash | Yes | Friendly People | 7790250.0 |
| 4 | tour_1004 | CHINA | 1-24 | NaN | 1.0 | 0.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Independent | ... | No | No | No | No | 7.0 | 4.0 | Cash | Yes | No comments | 1657500.0 |
5 rows × 23 columns
tz.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 4809 entries, 0 to 4808 Data columns (total 23 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 ID 4809 non-null object 1 country 4809 non-null object 2 age_group 4809 non-null object 3 travel_with 3695 non-null object 4 total_female 4806 non-null float64 5 total_male 4804 non-null float64 6 purpose 4809 non-null object 7 main_activity 4809 non-null object 8 info_source 4809 non-null object 9 tour_arrangement 4809 non-null object 10 package_transport_int 4809 non-null object 11 package_accomodation 4809 non-null object 12 package_food 4809 non-null object 13 package_transport_tz 4809 non-null object 14 package_sightseeing 4809 non-null object 15 package_guided_tour 4809 non-null object 16 package_insurance 4809 non-null object 17 night_mainland 4809 non-null float64 18 night_zanzibar 4809 non-null float64 19 payment_mode 4809 non-null object 20 first_trip_tz 4809 non-null object 21 most_impressing 4496 non-null object 22 total_cost 4809 non-null float64 dtypes: float64(5), object(18) memory usage: 864.2+ KB
tz.isnull().sum()
ID 0 country 0 age_group 0 travel_with 1114 total_female 3 total_male 5 purpose 0 main_activity 0 info_source 0 tour_arrangement 0 package_transport_int 0 package_accomodation 0 package_food 0 package_transport_tz 0 package_sightseeing 0 package_guided_tour 0 package_insurance 0 night_mainland 0 night_zanzibar 0 payment_mode 0 first_trip_tz 0 most_impressing 313 total_cost 0 dtype: int64
There are four categories with missing values
sns.pairplot(tz, hue='country')
<seaborn.axisgrid.PairGrid at 0x11ef33692b0>
sns.distplot(tz['total_cost']).set(title = 'Spending distribution')
C:\Users\user\Downloads\New folder\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
[Text(0.5, 1.0, 'Spending distribution')]
sns.heatmap(tz.corr(), annot= True)
<AxesSubplot:>
Questions:
What are the top 5 countries with the highest spending statistic ?
which age-group are the highest spenders and who are the over all highest spenders by travel with?
which country are have the most spending tourists?
what is the average number of nights a toursits spends in Tanzania mainland?
what is the average number of nights a toursits spends in Zanzibar?
what is the most prefered payment mode by tourists?
Highlight the Aspects of tourism that are more profitable and in which it is worthwhile to invest in
what is the most sort after food by tourists?
#top 5 countries with highest spending statistics
countries = tz[['country', 'total_cost']].head(10).groupby('country').sum()
countries
| total_cost | |
|---|---|
| country | |
| CHINA | 1657500.0 |
| INDIA | 2486250.0 |
| NIGERIA | 994500.0 |
| SOUTH AFRICA | 466140.0 |
| SWIZERLAND | 674602.5 |
| UNITED KINGDOM | 14441106.5 |
| UNITED STATES OF AMERICA | 3480750.0 |
plt.style.use('seaborn')
countries.plot(color='black', title= 'TOP 7 COUNTRIES', kind='bar')
<AxesSubplot:title={'center':'TOP 7 COUNTRIES'}, xlabel='country'>
#Age group with the highest spenders
Age = tz[['age_group', 'total_cost', 'travel_with']]
Age
| age_group | total_cost | travel_with | |
|---|---|---|---|
| 0 | 45-64 | 674602.5 | Friends/Relatives |
| 1 | 25-44 | 3214906.5 | NaN |
| 2 | 25-44 | 3315000.0 | Alone |
| 3 | 25-44 | 7790250.0 | Spouse |
| 4 | 1-24 | 1657500.0 | NaN |
| ... | ... | ... | ... |
| 4804 | 45-64 | 3315000.0 | Alone |
| 4805 | 25-44 | 10690875.0 | Spouse |
| 4806 | 1-24 | 2246636.7 | NaN |
| 4807 | 25-44 | 1160250.0 | Friends/Relatives |
| 4808 | 25-44 | 13260000.0 | Spouse |
4809 rows × 3 columns
Age_group = Age.groupby('age_group').sum()
Age_group
| total_cost | |
|---|---|
| age_group | |
| 1-24 | 3.379088e+09 |
| 25-44 | 1.498710e+10 |
| 45-64 | 1.537184e+10 |
| 65+ | 5.284068e+09 |
Age_group.plot(kind='bar', title='HIGHEST SPENDING AGE GROUP')
<AxesSubplot:title={'center':'HIGHEST SPENDING AGE GROUP'}, xlabel='age_group'>
Age_count = Age[['age_group']].value_counts()
Age_count
age_group 25-44 2487 45-64 1391 1-24 624 65+ 307 dtype: int64
#Country with the most spending tourist
cst =tz[['country', 'total_cost']].head(10)
cst
| country | total_cost | |
|---|---|---|
| 0 | SWIZERLAND | 674602.5 |
| 1 | UNITED KINGDOM | 3214906.5 |
| 2 | UNITED KINGDOM | 3315000.0 |
| 3 | UNITED KINGDOM | 7790250.0 |
| 4 | CHINA | 1657500.0 |
| 5 | UNITED KINGDOM | 120950.0 |
| 6 | SOUTH AFRICA | 466140.0 |
| 7 | UNITED STATES OF AMERICA | 3480750.0 |
| 8 | NIGERIA | 994500.0 |
| 9 | INDIA | 2486250.0 |
cst.plot(kind = 'bar', title = 'Country with the most spending tourist')
<AxesSubplot:title={'center':'Country with the most spending tourist'}>
#Average number of nights tourists spend at Tanzania Mainland
tz[['night_mainland']].mean()
night_mainland 8.488043 dtype: float64
An average of eight(8) nights are spent by tourists on Tanzania mainland
#Average number of nights tourists spend at Tanzania Mainland
tz[['night_zanzibar']].mean()
night_zanzibar 2.304429 dtype: float64
An average of two(2) nights are spent by tourists in Zanzibar
#Most preferred mode of payment by tourists
sns.catplot(data=tz, x= 'payment_mode', kind='count')
<seaborn.axisgrid.FacetGrid at 0x11eecff2400>
The most preferred mode of payment by the tourists is cash
#Aspects of tourism that are more profitable and in which it is worthwhile to invest in
sns.catplot(data= tz, y= 'main_activity', kind='count')
<seaborn.axisgrid.FacetGrid at 0x11eece7ac40>
The aspect of tourism that is more profitable and in which it is worthwhile to invest in is Wildlife tourism
#The most sort after food
sns.catplot(data=tz, x='package_food', kind='count' )
<seaborn.axisgrid.FacetGrid at 0x11eece7a040>
The most sort after food by the tourists is non package_food
#Missing values in travel_with
tz['travel_with'].fillna(value='Alone', inplace= True)
tz
| ID | country | age_group | travel_with | total_female | total_male | purpose | main_activity | info_source | tour_arrangement | ... | package_transport_tz | package_sightseeing | package_guided_tour | package_insurance | night_mainland | night_zanzibar | payment_mode | first_trip_tz | most_impressing | total_cost | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | tour_0 | SWIZERLAND | 45-64 | Friends/Relatives | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Friends, relatives | Independent | ... | No | No | No | No | 13.0 | 0.0 | Cash | No | Friendly People | 674602.5 |
| 1 | tour_10 | UNITED KINGDOM | 25-44 | Alone | 1.0 | 0.0 | Leisure and Holidays | Cultural tourism | others | Independent | ... | No | No | No | No | 14.0 | 7.0 | Cash | Yes | Wonderful Country, Landscape, Nature | 3214906.5 |
| 2 | tour_1000 | UNITED KINGDOM | 25-44 | Alone | 0.0 | 1.0 | Visiting Friends and Relatives | Cultural tourism | Friends, relatives | Independent | ... | No | No | No | No | 1.0 | 31.0 | Cash | No | Excellent Experience | 3315000.0 |
| 3 | tour_1002 | UNITED KINGDOM | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | No | 11.0 | 0.0 | Cash | Yes | Friendly People | 7790250.0 |
| 4 | tour_1004 | CHINA | 1-24 | Alone | 1.0 | 0.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Independent | ... | No | No | No | No | 7.0 | 4.0 | Cash | Yes | No comments | 1657500.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4804 | tour_993 | UAE | 45-64 | Alone | 0.0 | 1.0 | Business | Hunting tourism | Friends, relatives | Independent | ... | No | No | No | No | 2.0 | 0.0 | Credit Card | No | No comments | 3315000.0 |
| 4805 | tour_994 | UNITED STATES OF AMERICA | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | Yes | 11.0 | 0.0 | Cash | Yes | Friendly People | 10690875.0 |
| 4806 | tour_995 | NETHERLANDS | 1-24 | Alone | 1.0 | 0.0 | Leisure and Holidays | Wildlife tourism | others | Independent | ... | No | No | No | No | 3.0 | 7.0 | Cash | Yes | Good service | 2246636.7 |
| 4807 | tour_997 | SOUTH AFRICA | 25-44 | Friends/Relatives | 1.0 | 1.0 | Business | Beach tourism | Travel, agent, tour operator | Independent | ... | No | No | No | No | 5.0 | 0.0 | Credit Card | No | Friendly People | 1160250.0 |
| 4808 | tour_999 | UNITED KINGDOM | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | No | 4.0 | 7.0 | Cash | Yes | Friendly People | 13260000.0 |
4809 rows × 23 columns
#Missing values for total_male
tz['total_male'].fillna(method= 'bfill', inplace = True)
tz
| ID | country | age_group | travel_with | total_female | total_male | purpose | main_activity | info_source | tour_arrangement | ... | package_transport_tz | package_sightseeing | package_guided_tour | package_insurance | night_mainland | night_zanzibar | payment_mode | first_trip_tz | most_impressing | total_cost | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | tour_0 | SWIZERLAND | 45-64 | Friends/Relatives | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Friends, relatives | Independent | ... | No | No | No | No | 13.0 | 0.0 | Cash | No | Friendly People | 674602.5 |
| 1 | tour_10 | UNITED KINGDOM | 25-44 | Alone | 1.0 | 0.0 | Leisure and Holidays | Cultural tourism | others | Independent | ... | No | No | No | No | 14.0 | 7.0 | Cash | Yes | Wonderful Country, Landscape, Nature | 3214906.5 |
| 2 | tour_1000 | UNITED KINGDOM | 25-44 | Alone | 0.0 | 1.0 | Visiting Friends and Relatives | Cultural tourism | Friends, relatives | Independent | ... | No | No | No | No | 1.0 | 31.0 | Cash | No | Excellent Experience | 3315000.0 |
| 3 | tour_1002 | UNITED KINGDOM | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | No | 11.0 | 0.0 | Cash | Yes | Friendly People | 7790250.0 |
| 4 | tour_1004 | CHINA | 1-24 | Alone | 1.0 | 0.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Independent | ... | No | No | No | No | 7.0 | 4.0 | Cash | Yes | No comments | 1657500.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4804 | tour_993 | UAE | 45-64 | Alone | 0.0 | 1.0 | Business | Hunting tourism | Friends, relatives | Independent | ... | No | No | No | No | 2.0 | 0.0 | Credit Card | No | No comments | 3315000.0 |
| 4805 | tour_994 | UNITED STATES OF AMERICA | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | Yes | 11.0 | 0.0 | Cash | Yes | Friendly People | 10690875.0 |
| 4806 | tour_995 | NETHERLANDS | 1-24 | Alone | 1.0 | 0.0 | Leisure and Holidays | Wildlife tourism | others | Independent | ... | No | No | No | No | 3.0 | 7.0 | Cash | Yes | Good service | 2246636.7 |
| 4807 | tour_997 | SOUTH AFRICA | 25-44 | Friends/Relatives | 1.0 | 1.0 | Business | Beach tourism | Travel, agent, tour operator | Independent | ... | No | No | No | No | 5.0 | 0.0 | Credit Card | No | Friendly People | 1160250.0 |
| 4808 | tour_999 | UNITED KINGDOM | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | No | 4.0 | 7.0 | Cash | Yes | Friendly People | 13260000.0 |
4809 rows × 23 columns
#Missing values for total_female
tz['total_female'].fillna(method='bfill', inplace = True)
tz
| ID | country | age_group | travel_with | total_female | total_male | purpose | main_activity | info_source | tour_arrangement | ... | package_transport_tz | package_sightseeing | package_guided_tour | package_insurance | night_mainland | night_zanzibar | payment_mode | first_trip_tz | most_impressing | total_cost | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | tour_0 | SWIZERLAND | 45-64 | Friends/Relatives | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Friends, relatives | Independent | ... | No | No | No | No | 13.0 | 0.0 | Cash | No | Friendly People | 674602.5 |
| 1 | tour_10 | UNITED KINGDOM | 25-44 | Alone | 1.0 | 0.0 | Leisure and Holidays | Cultural tourism | others | Independent | ... | No | No | No | No | 14.0 | 7.0 | Cash | Yes | Wonderful Country, Landscape, Nature | 3214906.5 |
| 2 | tour_1000 | UNITED KINGDOM | 25-44 | Alone | 0.0 | 1.0 | Visiting Friends and Relatives | Cultural tourism | Friends, relatives | Independent | ... | No | No | No | No | 1.0 | 31.0 | Cash | No | Excellent Experience | 3315000.0 |
| 3 | tour_1002 | UNITED KINGDOM | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | No | 11.0 | 0.0 | Cash | Yes | Friendly People | 7790250.0 |
| 4 | tour_1004 | CHINA | 1-24 | Alone | 1.0 | 0.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Independent | ... | No | No | No | No | 7.0 | 4.0 | Cash | Yes | No comments | 1657500.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4804 | tour_993 | UAE | 45-64 | Alone | 0.0 | 1.0 | Business | Hunting tourism | Friends, relatives | Independent | ... | No | No | No | No | 2.0 | 0.0 | Credit Card | No | No comments | 3315000.0 |
| 4805 | tour_994 | UNITED STATES OF AMERICA | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | Yes | 11.0 | 0.0 | Cash | Yes | Friendly People | 10690875.0 |
| 4806 | tour_995 | NETHERLANDS | 1-24 | Alone | 1.0 | 0.0 | Leisure and Holidays | Wildlife tourism | others | Independent | ... | No | No | No | No | 3.0 | 7.0 | Cash | Yes | Good service | 2246636.7 |
| 4807 | tour_997 | SOUTH AFRICA | 25-44 | Friends/Relatives | 1.0 | 1.0 | Business | Beach tourism | Travel, agent, tour operator | Independent | ... | No | No | No | No | 5.0 | 0.0 | Credit Card | No | Friendly People | 1160250.0 |
| 4808 | tour_999 | UNITED KINGDOM | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | No | 4.0 | 7.0 | Cash | Yes | Friendly People | 13260000.0 |
4809 rows × 23 columns
#Missing values for most_impressing
tz['most_impressing'].fillna(value='no comments', inplace= True)
tz
| ID | country | age_group | travel_with | total_female | total_male | purpose | main_activity | info_source | tour_arrangement | ... | package_transport_tz | package_sightseeing | package_guided_tour | package_insurance | night_mainland | night_zanzibar | payment_mode | first_trip_tz | most_impressing | total_cost | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | tour_0 | SWIZERLAND | 45-64 | Friends/Relatives | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Friends, relatives | Independent | ... | No | No | No | No | 13.0 | 0.0 | Cash | No | Friendly People | 674602.5 |
| 1 | tour_10 | UNITED KINGDOM | 25-44 | Alone | 1.0 | 0.0 | Leisure and Holidays | Cultural tourism | others | Independent | ... | No | No | No | No | 14.0 | 7.0 | Cash | Yes | Wonderful Country, Landscape, Nature | 3214906.5 |
| 2 | tour_1000 | UNITED KINGDOM | 25-44 | Alone | 0.0 | 1.0 | Visiting Friends and Relatives | Cultural tourism | Friends, relatives | Independent | ... | No | No | No | No | 1.0 | 31.0 | Cash | No | Excellent Experience | 3315000.0 |
| 3 | tour_1002 | UNITED KINGDOM | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | No | 11.0 | 0.0 | Cash | Yes | Friendly People | 7790250.0 |
| 4 | tour_1004 | CHINA | 1-24 | Alone | 1.0 | 0.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Independent | ... | No | No | No | No | 7.0 | 4.0 | Cash | Yes | No comments | 1657500.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4804 | tour_993 | UAE | 45-64 | Alone | 0.0 | 1.0 | Business | Hunting tourism | Friends, relatives | Independent | ... | No | No | No | No | 2.0 | 0.0 | Credit Card | No | No comments | 3315000.0 |
| 4805 | tour_994 | UNITED STATES OF AMERICA | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | Yes | 11.0 | 0.0 | Cash | Yes | Friendly People | 10690875.0 |
| 4806 | tour_995 | NETHERLANDS | 1-24 | Alone | 1.0 | 0.0 | Leisure and Holidays | Wildlife tourism | others | Independent | ... | No | No | No | No | 3.0 | 7.0 | Cash | Yes | Good service | 2246636.7 |
| 4807 | tour_997 | SOUTH AFRICA | 25-44 | Friends/Relatives | 1.0 | 1.0 | Business | Beach tourism | Travel, agent, tour operator | Independent | ... | No | No | No | No | 5.0 | 0.0 | Credit Card | No | Friendly People | 1160250.0 |
| 4808 | tour_999 | UNITED KINGDOM | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | No | 4.0 | 7.0 | Cash | Yes | Friendly People | 13260000.0 |
4809 rows × 23 columns
from pandas_profiling import ProfileReport
profile = ProfileReport(tz)
profile
Summarize dataset: 0%| | 0/5 [00:00<?, ?it/s]
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#features for modelling
x = tz[['total_female',
'total_male','night_mainland','night_zanzibar'
]]
#Target variable or Prediction variable.
y = tz[['total_cost']]
#Train test split
from sklearn.model_selection import train_test_split
x_test,x_train, y_test,y_train = train_test_split(x,y, test_size=0.8, random_state=105)
#Creating and Training the model
from sklearn.linear_model import LinearRegression
#Instantiate model
lm = LinearRegression()
lm.fit(x_train,y_train)
LinearRegression()
print(lm.intercept_)
[4283710.80386905]
lm.coef_
array([[2142343.47631708, 693100.12934699, 50978.34380813,
362964.92797582]])
x_train.columns
Index(['total_female', 'total_male', 'night_mainland', 'night_zanzibar'], dtype='object')
prediction = lm.predict(x_test)
prediction
array([[ 5333659.33987294],
[10016757.31202075],
[ 9659908.90536385],
[ 5180724.30844856],
[16425606.4751851 ],
[ 6680945.99922677],
[ 5594667.5802325 ],
[ 7190729.43730806],
[ 5180724.30844856],
[11621552.05534841],
[13788160.6701879 ],
[ 8189699.62950382],
[ 5078767.6208323 ],
[15762719.70750957],
[ 7886445.93158824],
[ 9398900.66500429],
[ 6782902.68684303],
[12962039.34868405],
[ 9312476.22965833],
[ 7323067.78476564],
[12126271.06173319],
[ 5435616.0274892 ],
[ 9720302.98012336],
[10209489.73423784],
[ 9659908.90536385],
[ 5486594.37129733],
[12525565.87269938],
[ 6167662.40475976],
[ 8183784.05493818],
[34597173.64556116],
[ 5771867.75017929],
[ 6353226.21603552],
[ 9618346.2925071 ],
[ 5741486.09033797],
[ 9264114.25078339],
[ 5690507.74652984],
[ 6680945.99922677],
[ 6383607.87587684],
[ 6731924.3430349 ],
[ 9105062.69804009],
[15914120.75136554],
[ 5078767.6208323 ],
[ 7883829.56665505],
[27330088.92757099],
[ 7814702.60564344],
[ 9659908.90536385],
[10677027.7147631 ],
[11147214.70880496],
[ 5333659.33987294],
[ 6884859.37445929],
[10415851.17623368],
[ 7577959.50380628],
[ 5690507.74652984],
[ 7496599.50015683],
[ 5180724.30844856],
[ 4976810.93321604],
[11003695.40833195],
[ 7667884.09553797],
[ 7883829.56665505],
[ 9698453.25978088],
[ 5822846.09398742],
[ 6629967.65541864],
[10921933.51117596],
[10464381.45327848],
[ 8189699.62950382],
[10107533.04662158],
[ 6528010.96780239],
[ 5822846.09398742],
[14911449.45603077],
[10877473.58219329],
[ 7955404.59442999],
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sns.distplot(y_test- prediction/10**6)
C:\Users\user\Downloads\New folder\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). warnings.warn(msg, FutureWarning)
<AxesSubplot:ylabel='Density'>
#Looking at the Linear evaluation metrics we get:
from sklearn import metrics
#Average error of the model
metrics.mean_absolute_error(y_test,prediction)
7159079.5603594845
#Checkmating target errors by squaring
metrics.mean_squared_error(y_test,prediction)
114911704866686.25
np.sqrt(metrics.mean_squared_error(y_test,prediction))
10719687.722442582
r_squared = lm.score(x_test, prediction)
r_squared
1.0
from sklearn.metrics import r2_score
r2_score(y_test,prediction)
0.10424171803690241
from sklearn.svm import SVC
tz2= pd.read_csv('Test .csv')
tz2.head(20)
| ID | country | age_group | travel_with | total_female | total_male | purpose | main_activity | info_source | tour_arrangement | ... | package_food | package_transport_tz | package_sightseeing | package_guided_tour | package_insurance | night_mainland | night_zanzibar | payment_mode | first_trip_tz | most_impressing | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | tour_1 | AUSTRALIA | 45-64 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | Yes | Yes | 10 | 3 | Cash | Yes | Wildlife |
| 1 | tour_100 | SOUTH AFRICA | 25-44 | Friends/Relatives | 0.0 | 4.0 | Business | Wildlife tourism | Tanzania Mission Abroad | Package Tour | ... | No | No | No | No | No | 13 | 0 | Cash | No | Wonderful Country, Landscape, Nature |
| 2 | tour_1001 | GERMANY | 25-44 | Friends/Relatives | 3.0 | 0.0 | Leisure and Holidays | Beach tourism | Friends, relatives | Independent | ... | No | No | No | No | No | 7 | 14 | Cash | No | No comments |
| 3 | tour_1006 | CANADA | 24-Jan | Friends/Relatives | 2.0 | 0.0 | Leisure and Holidays | Cultural tourism | others | Independent | ... | No | No | No | No | No | 0 | 4 | Cash | Yes | Friendly People |
| 4 | tour_1009 | UNITED KINGDOM | 45-64 | Friends/Relatives | 2.0 | 2.0 | Leisure and Holidays | Wildlife tourism | Friends, relatives | Package Tour | ... | Yes | Yes | No | No | No | 10 | 0 | Cash | Yes | Friendly People |
| 5 | tour_1010 | DENMARK | 25-44 | NaN | 1.0 | 0.0 | Volunteering | Cultural tourism | Friends, relatives | Independent | ... | No | No | No | No | No | 55 | 0 | Cash | No | Good service |
| 6 | tour_1014 | GERMANY | 45-64 | NaN | 0.0 | 1.0 | Meetings and Conference | Mountain climbing | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | No | Yes | No | 9 | 0 | Cash | Yes | Friendly People |
| 7 | tour_1015 | RUSSIA | 24-Jan | Alone | 1.0 | 0.0 | Visiting Friends and Relatives | Beach tourism | Friends, relatives | Independent | ... | No | No | No | No | No | 26 | 3 | Cash | No | Friendly People |
| 8 | tour_1019 | GERMANY | 24-Jan | Alone | 1.0 | 0.0 | Leisure and Holidays | Cultural tourism | Friends, relatives | Independent | ... | No | No | No | No | No | 3 | 0 | Cash | No | Friendly People |
| 9 | tour_1020 | FRANCE | 24-Jan | Friends/Relatives | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Newspaper, magazines,brochures | Independent | ... | No | No | No | No | No | 30 | 9 | Cash | Yes | Friendly People |
| 10 | tour_1023 | SPAIN | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | No | No | Yes | 6 | 4 | Cash | Yes | Wildlife |
| 11 | tour_1025 | UNITED KINGDOM | 25-44 | Spouse and Children | 1.0 | 2.0 | Leisure and Holidays | Wildlife tourism | Radio, TV, Web | Package Tour | ... | Yes | Yes | Yes | Yes | No | 6 | 6 | Cash | Yes | Friendly People |
| 12 | tour_1031 | SWIZERLAND | 45-64 | Spouse and Children | 4.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Travel, agent, tour operator | Package Tour | ... | Yes | Yes | Yes | Yes | No | 10 | 7 | Cash | Yes | Wildlife |
| 13 | tour_1037 | UNITED STATES OF AMERICA | 24-Jan | Friends/Relatives | 1.0 | 1.0 | Scientific and Academic | Cultural tourism | Friends, relatives | Independent | ... | No | No | No | No | No | 30 | 0 | Cash | No | No comments |
| 14 | tour_1044 | GERMANY | 25-44 | Alone | 0.0 | 1.0 | Leisure and Holidays | business | Friends, relatives | Package Tour | ... | Yes | Yes | No | No | No | 6 | 0 | Cash | Yes | No comments |
| 15 | tour_1053 | CANADA | 45-64 | Spouse and Children | 4.0 | 1.0 | Visiting Friends and Relatives | Wildlife tourism | Radio, TV, Web | Independent | ... | No | No | No | No | No | 21 | 0 | Cash | No | Friendly People |
| 16 | tour_1056 | SPAIN | 25-44 | Friends/Relatives | 0.0 | 2.0 | Leisure and Holidays | Wildlife tourism | Newspaper, magazines,brochures | Independent | ... | No | No | No | No | No | 10 | 6 | Cash | Yes | Wonderful Country, Landscape, Nature |
| 17 | tour_106 | CHINA | 25-44 | Friends/Relatives | 2.0 | 2.0 | Visiting Friends and Relatives | Mountain climbing | Trade fair | Package Tour | ... | Yes | Yes | No | No | No | 7 | 0 | Cash | No | No comments |
| 18 | tour_1064 | UNITED KINGDOM | 25-44 | Spouse and Children | 4.0 | 1.0 | Visiting Friends and Relatives | Beach tourism | Friends, relatives | Independent | ... | No | No | No | No | No | 30 | 0 | Cash | No | Wonderful Country, Landscape, Nature |
| 19 | tour_1069 | UNITED KINGDOM | 25-44 | Spouse | 1.0 | 1.0 | Leisure and Holidays | Wildlife tourism | Radio, TV, Web | Package Tour | ... | No | Yes | Yes | Yes | No | 6 | 7 | Cash | Yes | Wildlife |
20 rows × 22 columns
tz2.corr()
| total_female | total_male | night_mainland | night_zanzibar | |
|---|---|---|---|---|
| total_female | 1.000000 | 0.288933 | 0.015265 | 0.078020 |
| total_male | 0.288933 | 1.000000 | -0.035880 | 0.020622 |
| night_mainland | 0.015265 | -0.035880 | 1.000000 | 0.516262 |
| night_zanzibar | 0.078020 | 0.020622 | 0.516262 | 1.000000 |
sns.heatmap(tz2.corr(), annot= True)
<AxesSubplot:>
lm.fit(x,y)
LinearRegression()
y_pred = lm.predict(x_test)
y_pred
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**-After deep analysis, we observed that the most preferred mode of payment by the tourists is cash. We recommend that the Government of Tanzania should create a limit for the possession of cash on one's self, to avoid instances of robbery and pick-pocketing.
**We also observed that the aspects of tourism that are more profitable and in which it is worthwhile to invest in are Wildlife tourism, Beach tourism & Hunting tourism. We recommend that a lot of attention and effort, should be put into Diving and sport fishing and the other less profitable tourist attractions, to also make them worthwhile for investments and tourism